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okay, so this is not so big to many of you , but today , i was just bored and tired of doing any marketing , cos nothing seemed to have been working. so i decided to do a search (i searched for quite a number of keywords) like error tracking for supabase , error tracking for next Js , but my saas didnt rank (if you dont know what im building , im working on a dead simple error tracker that notifies you when something breaks in prod, no bloated dashboards or config hell) , i built this cos sentry had too much noise and i just wanted something that lets me know when there's an issue in prod.

so i decided to do a search on "error tracking for shipfast(by marc lou)" and guess what ?? Bugmail was the first recommended result , now i dont know how this worked, i mean ive done some seo and stuff but i wasnt expecting it , and now i feel like im back , which is funny cos i didnt make a sale , i didnt onboard a new user yet i feel like o have conquered , life of a founder , i guess

i just wanted to share this here, also if you have any advice on how to rank higher on GSC and how to nail this marketing thing , any advice or feedback will be valuable

in the meantime , incase you want to check out what ive built;

you can check it out here ; https://www.bugmail.site

I've been using Twitter / X for a good decade now, and while I've found it's a great place to connect with native app dev communities (I'm well connected with the React Native scene), I really struggle to connect with any web devs.

There are a few big names like Adam Wathan who are pretty active on Twitter of course, but considering how widespread web dev is, I see precious few up-and-coming web devs coding in public.

So, where are they? I have explored BlueSky a bit, but again it feels a bit like tumbleweeds (though maybe that's just my luck as a small account).

Are web devs more old-school, posting on bulletin boards and forums? Or is X still the answer, and I'm just getting aggressively packed into a different bubble?

… Or is it all realtime communication, like Slack and Discord, these days?

"Debugging is twice as hard as writing the code in the first place. Therefore, if you write the code as cleverly as possible, you are, by definition, not smart enough to debug it"

This has been a timely PSA.

I'm one of the developers behind CiderStack. It's a local-first macOS VM tool for Apple devs who need clean installs, older Xcodes, or a safe place to test things without bricking their main machine. Spin up → snapshot → break things → delete → repeat. Why no subscription? CiderStack runs on your hardware. It doesn't need my servers. It doesn't phone home. So why would you pay me every month? I'm tired of SaaS fees for tools that could easily be a one-time purchase. Tired of losing access to software because a card expired. Tired of workflows held hostage behind subscriptions. Buy it once. Own it forever. Free updates for the major version. That's the deal. Who it's for:

Solo devs testing across macOS versions without buying multiple Macs IT admins safely testing betas and MDM profiles before rollout CI/CD teams spinning up ephemeral macOS runners Homelabbers with a rack of Mac minis (we built this for people like us)

14-day free trial, no account required. Just shipped v1.0.3 (found a few bugs pretty early on) Happy to answer questions.

Running OpenClaw with Anthrophic API and it burned through ~USD 50 in one day.

What are other OpenClaw users seeing? Anyone found effective ways to cut costs (model tiering, caching, etc.)?

Hi everyone,

I’m building Persona, a platform to delegate email scheduling to AI. Lately, I’ve been working hard to get those first users on board, but it’s been quite challenging.

I’ve already tried the typical strategies that everybody talks about: cold email, LinkedIn InMail, careful targeting, decent copy. It’s mostly been a dead end. Low open rates, almost no replies.

At this point, I’m not looking for the usual advice you see in blog posts or on reddit. I’m specifically curious about unconventional or non-obvious things that actually worked for you early on, especially things that felt a bit scrappy, weird, or counterintuitive at the time.

If you’ve been through this phase, what genuinely worked and got you your first users?

Wanted to gather some stories about people who were fired because of AI. Not a generic "reorg", what they say in the press release, but honestly, because of AI. Proves?

Currently the GitHub status page says there is a "Major Outage" for GitHub Actions.

https://www.githubstatus.com/

This is as of 19:58 UTC / 11:58 PST on 2-Feb-2026

Currently, I’m working on a web app for managing documents, databases, and whiteboards—the typical app that aims to be like Notion.

However, right now I’m facing the dilemma of creating a plan with AI usage limits, since the idea is to make it more agentic: able to edit and query context across an entire workspace and move it into a document, for example, maybe draw something on a whiteboard, etc. Still, I have the feeling that consumption could easily get out of hand. I plan to use DeepSeek for AI chat, but use Gemini 3 Flash for agentic usage and editing because it’s more intelligent. Lately, I’ve seen how many core AI apps have shifted their pricing models from per-request pricing to a fixed usage limit, but I’m unsure whether that’s frowned upon, creates a less pleasant user experience, or maybe even gives the feeling that you’re not getting what you paid for. So I’d like to hear some opinions on what decision I should make.

I’m working on infrastructure to solve retry storms and outages. Before I go further, I want to understand what people are actually doing today. Compare solutions and maybe help someone see potential solutions. The problems:

Retry storms - API fails, your entire fleet retries independently, thundering herd makes it worse.

Partial outages - API is “up” but degraded (slow, intermittent 500s). Health checks pass, requests suffer.

What I’m curious about: ∙ What’s your current solution? (circuit breakers, queues, custom coordination, service mesh, something else?) ∙ How well does it work? What are the gaps? ∙ What scale are you at? (company size, # of instances, requests/sec)

I’d love to hear what’s working, what isn’t, and what you wish existed.

I’ve been thinking about why dead code detection (and static analysis in general) feels so unreliable in Python compared to other languages. I understand that Python is generally dynamic in nature.

In theory it should be simple(again in theory): parse the AST, build a call graph, find symbols with zero references. In practice it breaks down quickly because of many things like:

1. dynamic dispatch (getattr, registries, plugin systems)

2. framework entrypoints (Flask/FastAPI routes, Django views, pytest fixtures)

3. decorators and implicit naming conventions

4. code invoked only via tests or runtime configuration

Most tools seem to pick one of two bad tradeoffs:

1. be conservative and miss lots of genuinely dead code

or

2. be aggressive and flag false positives that people stop trusting

What’s worked best for me so far is treating the code as sort of a confidence score, plus some layering in limited runtime info (e.g. what actually executed during tests) instead of relying on 100% static analysis.

Curious how others handle this in real codebases..

Do yall just accept false positives? or do yall ignore dead code detection entirely? have anyone seen approaches that actually scale? I am aware that sonarqube is very noisy.

I built a library with a vsce extension, mainly to explore these tradeoffs (link below if relevant), but I’m more interested in how others think about the problem. Also hope I'm in the right channel

Repo for context: https://github.com/duriantaco/skylos

I'm someone who got into building/programming in early 2025, when vibe coding tools became more usable. Since then, I'd like to think that I have developed a lot as a programmer, but I still have this deep sense of imposter syndrome / worry that AI is too much of a crutch and I'm not really learning.

I have shipped a few projects, I always review AI-suggested code, do daily coding practice without AI, watch youtube videos, etc. but still don't know if I'm striking the right balance or whether I can really call myself a programmer.

I often see people say that the solution is to just fully learn to code without AI, (i.e, go "cold turkey"), which may be the best, but I wonder if the optimal path is somewhere in between given that AI is clearlly changing the game here in terms of what it means to be a programmer.

I'm curious how you have all handled this balancing act in the past few years. More concretely, what strategies do you use to both be efficient and able to ship / move quickly while ensuring you are also taking the time to really process and understand and learn what you are doing?

Hi all,

I've noticed that container images are getting bigger (particularly with AI related images). I was annoyed the pricing of my cloud provider so I quickly whipped something up.

It uses Cloudflare R2 + workers which is the lowest cost way to do this (AFAICT). However, due to how Cloudflare works, I had to change the push side to do it (pull side remains the same). I don't mind this personally (simplifies things a lot for typical use cases imo), but not sure what others would think. I am dog-fooding it right now and it is working fine but wondering if there is any interest in the community more broadly? As a SaaS, $5/month would cover 100GiB storage easily. Egress has a non-zero associated cost, but I think would be noise for most use cases. Alternately it could be something you run in your own CF account perhaps. It is extremely fast as well since it leverages Cloudflare's edge / CDN. So, faster, cheaper but a necessarily a little bit different to take advantage of things.

Anyway, if anyone has any thoughts / feedback on this approach, please let me know.

My Google account has been suspended from GCP since March 2024.

I have submitted multiple appeals through ts-consult@google.com over 2 years. Every time I get the same automated template asking me to explain, I reply with details, then nothing. No human ever responds.

Case: #1-8622000037271

Timeline: - March 2024: Suspended, appeal submitted - April 2024: Automated requests for info, I replied - Nov 2024: More automated emails, I replied again - Dec 2024 - now: Complete silence

I am a CS researcher at UC Berkeley. This has seriously impacted my work.

Has anyone successfully gotten Google to review a GCP suspension appeal? How do you reach a human?

I was revising my proposal defense and kept feeling like I was repeating the same term. In a typical LaTeX project split across many .tex files, it’s awkward to get a quick, clean word-frequency view without gluing everything together or counting LaTeX commands/math as “words”.

So I built latex-wc, a small Python CLI that:

- extracts tokens from LaTeX while ignoring common LaTeX “noise” (commands, comments, math, refs/cites, etc.)

- can take a single .tex file or a directory and recursively scan all *.tex files

- prints a combined report once (total words, unique words, top-N frequencies)

Fastest way to try it is `uvx latex-wc [path]` (file or directory). Feedback welcome, especially on edge cases where you think the heuristic filters are too aggressive or not aggressive enough.

Hi HN,

I’m currently rethinking our hiring process. Like many of you, I feel that traditional algorithmic tests (LeetCode style) are becoming less relevant now that LLMs can solve them instantly. Furthermore, prohibiting AI during interviews feels counter-productive; I want to hire engineers who know how to use these tools effectively to multiply their output.

I am designing a new evaluation framework based on real-world open-source work, and I would love the community’s feedback on whether this sounds fair, effective, or if I’m missing something critical.

The Core Philosophy: We shouldn't test if a candidate can write syntax better than an AI. We should test if they can guide, debug, and improve upon an AI's output to handle the "last mile" of complex engineering.

The Proposed Process:

1. Task Selection (Real World Context) Instead of synthetic puzzles, we select open issues or discussions from public GitHub repositories that share a tech stack with our product.

    Scope: 2–4 hours.

    Types: Implementing a feature based on a discussion, fixing a bug, or reviewing a PR (specifically one that was eventually rejected, to test "taste").

    Ambiguity: Adjusted for seniority. Junior roles get clear specs; senior roles get vague problem statements requiring architectural decisions.

    The Filter: If the AI solves it perfectly on the first try, we discard the task.

    The Sweet Spot: We are looking for tasks where the AI gets 80% right but fails on edge cases, context integration, or complex logic. The problem setup should not be too easy or too hard.

How We Evaluate (The "AI Delta"):

We aren't just looking at the final code. We analyze the "diff" between the Candidate’s process and our "AI Baseline":

    1. Exploration Strategy: How does the candidate "load context"? Do they blindly paste errors, or do they guide the AI to understand the repository structure first? We look for a clear understanding of the existing codebase.

    2. Engineering Rigor (TDD): Does the candidate push the AI to generate a test plan or reproduction script before generating the fix? We value candidates who treat the AI as a junior partner that needs verification.

    3. The "Last 10%" (Edge Cases): Since we picked tasks where AI fails slightly, we look at how the candidate handles those failure modes. Can they spot the boundary conditions and logic errors that the LLM glossed over?

    4. Documentation Hygiene: We specifically check if the candidate instructs the AI to search existing documentation and—crucially—if they prompt the AI to update the docs to reflect the new changes.

    5. Engineering Taste (The Rejected PR): For the code review task, we ask them to analyze a PR that was rejected in the real world (without telling them). We want to see if their reasoning for rejection aligns with our team's engineering culture (maintainability, complexity, clarity, etc.).

    Is analyzing the "Chat History" too invasive, or is it the best way to see their thought process in 2026?

    For those of you hiring now, how do you distinguish between a "prompt kiddie" and a senior engineer who is just very good at prompting?

    Does the 2-4 hour time commitment feel reasonable for a "take-home" if the tooling makes the actual coding faster?

(Full disclosure: In the spirit of this topic, this post was composed by AI based on my draft notes.)

Especially interested in people using AI for brownfield development, but generally interested in if people are continuing down the spec driven path, or if agents + skills/prompts/mcp/agents.md/something else is filling the niche spec driven development was trying to capture.

Question was prompted by seeing spec kit have no commits for over a month an no obvious integration with GitHub’s new agents integration.

Facebook's feed is nonfunctional. Only some people get notification, even though they have notification for all messages turned on. Only some get the newest posts in their main feed. Sometimes I do get notifications, but only long after an urgent message was posted.

Has anybody successfully moved their local community off of Facebook groups?

I'm thinking about neighbor conversations/events, daycare, kindergarten, kids' classmates, sporting communities, etc.

If so, where did you go? Did you build something yourself or do you self-host some open source project? Did you find a good paid alternative?

Hey everyone,

I’m currently in the middle of building a feedback tool for my own SaaS projects, and I wanted to gut-check a UX/Architecture theory with this community.

When I was looking for existing tools to handle feature requests and user feedback, I noticed a depressing pattern. Almost every "Feedback Board" I visited felt boring and like a ghost town.

You know the vibe:

You land on the page.

There are a dozen posts from 8 months ago and a few recent ones. Most are tagged "Under Review" (and have been for a year). It feels silent. Static. Dead.

As a user, my immediate reaction is: "Nobody is home. Why bother typing my idea?"

The Theory: I think the problem is that we treat feedback like tickets instead of conversations. We are building "suggestion boxes" in dark corners when we should be building "town halls."

The Tech Stack / Approach: I decided to code a solution that tries to flip this using a Nuxt 4, focusing heavily on real-time presence. I want to know if you guys think this technical overhead is worth it, or if I’m over-engineering a simple problem.

Here is how I’m handling it:

Forcing "Aliveness" with Real-Time State: Instead of a standard CRUD list, I’m using Server-Sent Events (SSE) to sync user presence.

The Goal: Show "Staff Online" indicators and "User is typing..." bubbles in the feed.

The Tech: Leveraging Vue's reactivity system to broadcast updates instantly. If a user is viewing a post, I want others to see their avatar there. I want to psychologically trick the user into feeling like they are entering a live room, not filling out a form.

Killing the Silos (Unified Inbox): I realized that having "Support" in email and "Feedback" on a board creates a disconnect.

I’m building a unified admin stream that merges Email (via parsed webhooks), Chat, and User post submissions into a single view.

It’s designed to be keyboard-first so admins can clear the queue faster, keeping the "Last Activity" timestamps fresh.

The Question for you builders/users:

If you landed on a roadmap/feedback page and saw a green dot saying "Staff is Online" or saw live activity happening (comments updating, viewing counters ticking up), would that actually make you more likely to engage?

Or do you prefer the standard "Submit and Forget" style boards like Canny/Uservoice?

I’m squashing the last few bugs (mainly edge cases with the real-time sync) right now, but I’m really curious if the "Dead Board" vibe bothers anyone else, or if it’s just me.

In other words, what's the benefit for the average joe(s) / humanity as a whole?

To make AI agents more efficient, we need to build feedback loops with real systems: deployments, logs, configs, environments, dashboards.

But this is where things break down.

Most modern apps don’t have fine-grained permissions.

Concrete example: Vercel. If I want an agent to read logs or inspect env vars, I have to give it a token that also allows it to modify or delete things. There’s no clean read-only or capability-scoped access.

And this isn’t just Vercel. I see the same pattern across cloud dashboards, CI/CD systems, and SaaS APIs that were designed around trusted humans, not autonomous agents.

So the real question:

How are people actually restricting AI agents in production today?

Are you building proxy layers that enforce policy? Wrapping APIs with allowlists? Or just accepting the risk?

It feels like we’re trying to connect autonomous systems to infrastructure that was never designed for them.

Curious how others are handling this in real setups, not theory.

I am curious I am not sure if AI is just hype, I use it for software and a few other things. But looking at so many people talking about AGI when the best models can't even answer simple stuff correctly, fail at tool use, are vulnerable to all types of injection attacks that don't make sense.

I don't know if the investments in AI are worth it but am I blind for not seeing any hope for AGI any time soon.

Agentic AI is interesting perhaps but I hardly have had it work perfectly, I have to hold it's hand at everything.

People making random claims about AGI soon is really weakening my confidence in AI in general. Given I haven't seen much improvements in last few years other than better tools and wrappers, and models that work better with these tools and wrappers.

I’ve noticed that most physical scientific and graphing calculators are easily outdone in terms of performance, capability and ease of use by the likes of Desmos and the default calculators on OS’es like the iOS, Android, and Windows.

It kind of makes me wonder whether people still use physical calculators from Texas Instruments, Casio, etc

If you do, I’d love to know why and how it is different/better for you than the ones I’ve mentioned and others like them and vice verse.

Cheers!

Started with actions runners not picking up new jobs and now it spread to other services. - https://www.githubstatus.com/

Could not post it earlier as it was flagged as a duplicate of an incident from last month.

Hello those who still read forums.

I have recently graduated from a college and started working as a junior dev (trying to consume as much knowledge from senior colleagues as I can now) and it seems that the real world is kind of a different story compared to the college practice.

In the college we've been taught about design patterns and all these responsibilities like domain, application, infrastructure, UI. Domain should never depend on infrastructure or application layer and so on. But the projects I got have domain that depend on infrastructure and another one where application has a reference directly to infrastructure and been told that this is correct implementation... doh..

I think I was kind of a good at listening for the lectures, but I now am doubting about, whether it was worth learning stuff at all lol since it's so controversial out there. I am, of course, in no position to question senior dev, but what do you guys think - is it really normal that all the college so called "best practices" go straight to the trash bin or am I just misunderstanding the real-work-like context?

I'm genuinely curious. I've read a lot of stories of people adding it to group chats, using it as a remote coding and research assistant, or getting it to clean up folders and inboxes. This is all interesting, but nothing that couldn't have been accomplished with the tooling that was already in place.

I'd like to hear stories like "it accurately filed my taxes", "it organized and booked all tickets for my trip from A to B", or "it successfully executed a cold email campaign with a 30% open rate". These stories must be out there - would you mind sharing them?

Salutations HN!

I’ll keep it simple, has anyone known of or been a part of a co-op of software engineers? And if so, how did it go?

I’m curious because it seems like the vast majority of early capital raises seem to go to paying for software development, and a small group of engineers could (in theory) output a few million dollars worth of value. It might not be attractive to VCs but it could probably form a business that lets coop members lead very comfortable lives.